Radiation therapy treatment planning requires accurate dose calculations in order to maximize the tumoricidal effects of precisely directed radiation while minimizing doses delivered to nearby normal tissues. Monte Carlo (MC) radiation transport is the only method which is capable of fulfilling this need in all situations of clinical interest. However, MC dose computations must be run until statistical fluctuations ("noise") in the resulting dose distributions are adequately reduced. We have discovered that MC precision can be greatly improved through statistical estimation of the actual noise-free underlying dose distribution from the noisy simulation output, a process we term "denoising." We have shown that denoising is capable of reducing MC calculation times at least several-fold. We propose to investigate the application of denoising to conformal photon therapy and intensity modulated radiation therapy (IMRT) dose calculations. Metrics for quantifying denoising performance will be developed under Specific Aim #1. A benchmark test suite of MC dose distributions, including photon beam, IMRT pencil beam, and optimized IMRT dose distributions, will be developed under Specific Aim #2. Wavelet shrinkage threshold denoising will be developed under Specific Aim #3. Denoising using spatially adaptive iterative filtering will be developed under Specific Aim #4. The relative performance and clinical acceptability of the two denoising methods will be tested against the benchmark test suite with the metrics developed under Specific Aim #1. Under Specific Aim #5 we propose to use discrete wavelet transforms to denoise and compress three dimensional MC- generated pencil beam (PB) dose distributions, and to efficiently compute IMRT fluence-weighted PB dose distributions. Specific Aim #6 will establish maximum MC PB noise levels acceptable for IMRT treatment planning. We hypothesize that optimal denoising algorithms for external photon beams and IMRT PBs will decrease MC computation times by at least a factor of 5-10. We further hypothesize that wavelet-based dose computation methods will: (a) enable use of accurate MC-based PB dose distributions for IMRT treatment planning, (b) apply to MC or any other PB dose calculation algorithm, and (c) be far more computationally efficient than complete dose recalculations at each IMRT optimization iteration. These results would achieve our overall goal of increasing the clinical effectiveness of radiation therapy treatment planning.